ArticleJournal of digital imaging2021
Proactive Construction of an Annotated Imaging Database for Artificial Intelligence Training.
Article in Journal of digital imaging, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.
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Who cites it
13 citing papers in PubMed.
- Pathology-CoT: learning visual chain-of-thought agents from expert whole-slide image diagnosis behaviour.Nature biomedical engineering · 2026Article
- An open-source platform for structured annotation and computational workflows in digital pathology research.Scientific reports · 2025Article
- Generating dermatopathology reports from gigapixel whole slide images with HistoGPT.Nature communications · 2025Article
- Bibliometric analysis of the application of artificial intelligence in orthopedic imaging.Quantitative imaging in medicine and surgery · 2025Article
- Impact of Radiologist Experience on AI Annotation Quality in Chest Radiographs: A Comparative Analysis.Diagnostics (Basel, Switzerland) · 2025Article
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- MIDAS: a new platform for quality-graded health data for AI-enabled healthcare in India.Nature medicine · 2024Article
- A Systematic Comparison of Task Adaptation Techniques for Digital Histopathology.Bioengineering (Basel, Switzerland) · 2023Article
- Data-driven color augmentation for H&E stained images in computational pathology.Journal of pathology informatics · 2023Article
- Pan-tumor CAnine cuTaneous Cancer Histology (CATCH) dataset.Scientific data · 2022Article
- Integrating Biological and Radiological Data in a Structured Repository: a Data Model Applied to the COSMOS Case Study.Journal of digital imaging · 2022Article
- Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations.NPJ digital medicine · 2022Article
Corrections and comments
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Authors and funding
14 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) holds much promise for enabling highly desired imaging diagnostics improvements. One of the most limiting bottlenecks for the development of useful clinical-grade AI models is the lack of training data. One aspect is the large amount of cases needed and another is the necessity of high-quality ground truth annotation. The aim of the project was to establish and describe the construction of a database with substantial amounts of detail-annotated oncology imaging data from pathology and radiology. A specific objective was to be proactive, that is, to support undefined subsequent AI training across a wide range of tasks, such as detection, quantification, segmentation, and classification, which puts particular focus on the quality and generality of the annotations. The main outcome of this project was the database as such, with a collection of labeled image data from breast, ovary, skin, colon, skeleton, and liver. In addition, this effort also served as an exploration of best practices for further scalability of high-quality image collections, and a main contribution of the study was generic lessons learned regarding how to successfully organize efforts to construct medical imaging databases for AI training, summarized as eight guiding principles covering team, process, and execution aspects.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.